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08/02/2023
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
We show that large language models (LLMs) are remarkably good at working...
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05/24/2023
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Generating differentially private (DP) synthetic data that closely resem...
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03/22/2023
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Artificial intelligence (AI) researchers have been developing and refini...
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03/20/2023
Capabilities of GPT-4 on Medical Challenge Problems
Large language models (LLMs) have demonstrated remarkable capabilities i...
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07/12/2022
Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes
Most pregnancies and births result in a good outcome, but complications ...
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06/30/2022
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Machine learning (ML) interpretability techniques can reveal undesirable...
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02/22/2022
Differentially Private Estimation of Heterogeneous Causal Effects
Estimating heterogeneous treatment effects in domains such as healthcare...
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12/06/2021
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Recent strides in interpretable machine learning (ML) research reveal th...
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06/17/2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
We show that adding differential privacy to Explainable Boosting Machine...
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09/19/2019
InterpretML: A Unified Framework for Machine Learning Interpretability
InterpretML is an open-source Python package which exposes machine learn...
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07/02/2018
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
Differential privacy has emerged as the main definition for private data...
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03/24/2018
